1. Field of the Invention
The dual waveband signal processing system (DWSPS) is a system for processing detected emissions (infrared or similar spectral wavebands, X-rays and polarized) by a sensor to identify targets or objects with a particularly unique spectral characteristic against a complex cluttered background.
2. Description of Related Art
Infrared sensors have been developed over the past 20 to 25 years to achieve automatic target detection. Although some measures of success has been achieved for the systems, the performance has typically been significantly less than that promised. One of the reasons for this discrepancy is the problem of detecting the presence of a real target in the presence of background clutter. Where the background is the sky, the clutter is self-radiation and solar-scattering from the clouds. With the earth as a background, the clutter is produced by the temperature and emissivity variance of the ground and solar reflections from the surfaces of water, metal, or glass. Also, industrial activity produces very hot radiations and gaseous emissions from smoke stacks that can produce very high background environments. Unfortunately the background is not uniform and the spatial clutter interferes with the detection process. The operational environment of looking from an aircraft at the ground is one of the most severe infrared (IR) background emissions (ground clutter).
In the current technology, detected images are processed so as to extract target information at the receiver. Signal processing generally involves photodetection followed by some form of baseband waveform processing that is dependent on the models of photodetector outputs. This technology involves the taking of the ratios of the data in adjacent spectral bands. However, in the infrared spectrum, for instance, these methods are limited to an approach of color-ratioing after threshold discrimination on a single-color filtered band which lacks the high degree of sensitivity needed to detect the minute changes in IR intensity present in such cases as the vegetation utilized in the production of narcotics.
Another approach with two spectral bands is to use the high correlation between bands to predict the data in one band from the data in the other (linear regression).
One current method of processing the electro-optical signal is three-dimensional filtering. Three-dimensional filtering is a straightforward extension of one- and two-dimensional filter theory. The one-dimensional case derives optical filters in a single domain (usually time) for maximization of a temporal signal-to-noise ratio. Two-dimensional filter theory likewise derives optimal filters in two-dimensions (usually spatial coordinates) for a spatial signal-to-noise ratio. Three-dimensional filtering extends the concept of observing a spatial area over a fixed time period where it is natural to associate the dimensions of space, area and time to maximize a defined signal-to-noise ratio for a target with a given velocity.
A more recent method utilizes a six band sensor and a methodology for applying an eigenvalue transformation and principle component projection operator to remove most of the temperature variability and spectrally non-sensitive emissivity variations from the data.